RAG 2.0 Security Guide: Protect the End-to-End System

RAG 2.0 expands the security review beyond a retrieval call followed by a prompt. The term can mean an end-to-end system whose retriever and generator are developed together, or more broadly an advanced pipeline with reranking, feedback, agents, knowledge graphs, and tool use. A useful security guide must state which meaning it uses before drawing the boundary.

What RAG 2.0 means

Contextual AI introduced RAG 2.0 as its end-to-end approach for developing robust enterprise AI. The important architectural claim is that retrieval and generation are treated as one jointly developed system rather than independent off-the-shelf parts. Training, evaluation, and feedback can therefore influence the retriever, reranker, context construction, and generator together.

That definition is vendor-originated, not an industry standard. Use it precisely: a system does not become RAG 2.0 merely because it adds another retrieval step. In this guide, RAG 2.0 means an end-to-end retrieval and generation system whose components, data, and evaluation loop can change one another's behavior.

Why the term has a wider meaning in practice

Current usage also applies RAG 2.0 to agentic or advanced RAG: query planning, multiple retrievers, reranking, iterative retrieval, knowledge graphs, memory, and tools. These architectures are not identical to Contextual AI's definition, but they create a shared security consequence: more identities, data transformations, feedback paths, and downstream effects belong inside the review.

Document the definition used by your team. Name the ingestion pipeline, indexes, retrievers, rerankers, generator, evaluators, feedback stores, agents, and tools. If MCP connects a retriever or action, use the MCP protocol architecture to identify the client-server handoff, then add the actual tenant and downstream system.

Secure the end-to-end optimization loop

Training and feedback data can become a control input in an end-to-end system. Record who may add examples, labels, preference data, and evaluation results; version those inputs; and prevent an untrusted user from silently teaching the system to prefer a poisoned source or unsafe action. A rollback must restore the affected retriever, generator, evaluation set, and policy versions that moved together.

Do not treat an aggregate quality score as security evidence. Keep adversarial cases for poisoned documents, manipulated feedback, unauthorized sources, and an apparently helpful answer that proposes an unsafe tool call. The pass condition includes the candidate set, context, output, policy decision, and target state.

Preserve identity and provenance across retrieval

Apply caller and tenant filters before candidates enter the model context. Every source needs an owner, version, sensitivity label, allowed audience, and deletion state. Test two identities against a canary document: the denied identity must not receive it as a candidate, context fragment, citation, answer, or generated argument.

Retrieval services often use broad connector accounts. The agent credential leak controls guidance is relevant when a token or connection string appears in a prompt trace or evaluation export. Use synthetic credential canaries and keep raw protected chunks out of long-lived debug artifacts.

If the team cannot reconstruct which identity and filter produced the candidate set, stop the review there. Repair that evidence path before interpreting the model response or testing a downstream action.

Keep retrieved content out of the authority layer

A highly relevant document can still be hostile. Preserve its source label and pass it as data; do not let it add tools, rewrite approval rules, change a recipient, or write directly into long-term memory. The OWASP LLM Top 10 supplies threat categories for prompt injection, data exposure, and system interaction, while the application must enforce its own identity and effect rules.

For agentic RAG, authorize the action after retrieval using the initiating user, tenant, tool, destination, and normalized arguments. A document's relevance score is never permission to act.

Test the wider RAG 2.0 boundary

Run separate cases for a poisoned source, revoked document, cross-tenant query, manipulated feedback item, unsafe memory write, and retrieved instruction that proposes a tool action. Retain source and model versions, eligible candidates, policy decision, safe output evidence, and downstream state. When a real trace takes a path missing from the architecture, update both the RAG 2.0 definition and its regression suite.

Treat AgentGuard's stated inability to fully monitor or block all third-party MCP server runtime calls as a boundary in the RAG diagram, not as an unmeasured pass. For an exact retriever connector, MCP server, plugin, skill, or agent, AgentGuard documentation can establish whether Deep Scan is applicable before admission.

Keep any component result separate from corpus permissions, training-data provenance, tenant filters, feedback integrity, and the end-to-end optimization loop. Those application-owned results belong in the RAG test record.